AIブームは本物か、持続するか――Anthropic S-1、SpaceXのネオクラウド、フロンティアAI規制案

All right, so we're back with my friend Evangelos Zemoudis. His blog is at corporateinnovation.co. We are recording this on the morning of October 30th, 2026. Episode notes, including all the resources we may cite, are on the dataexchange.media. And please hit the subscribe button if you're on YouTube and subscribe wherever you get your podcasts. So topic number one, this is a big week in AI. Antropic released their, I guess, their S1, right? 134 megabytes. Yeah, yeah. And what I did was I basically just looked through it on the day off and just pulled out things I like and things I didn't like, but I haven't really read much.

So here, this is the first time I'm going to kind of bounce off some ideas with someone else. So, okay, so start with things I like. First one, Evangelist, is the growth. Growth seems hard to fake. Exceptional. Very good. I agree. And I don't think there's much to argue that the growth numbers are out of this world, right? So they're insane. We also need to be watching, though, for how that growth continues. But I agree with you. Great, great numbers. And the second item I like, which I don't actually, in retrospect, I kind of just put it on my post, but I don't know if you've had a chance to vet what I said, but I said the unit economics seemed to have improved. So at least on the S1, they said it used to be 40 cents of revenue per dollar spent on compute, and that figure was now $2.08.

$2.08, yeah. I agree that this is – I think we will need to see where the gross margins go because that was another very nice surprise, if I can call it. And I think as people scrutinize what's included in that gross margin and how we can continue, that could be another nice surprise if these numbers stick. So for the listeners who may find this surprising, in light of the fact that, you know, you've got these open weights models, So isn't there price pressure? How come the unit economics got better? I don't think that we have seen. Remember, the numbers that they report go through first half of 2026. I don't think, I'll speak now for what we're seeing in the enterprise. I don't think that the open wage model has had the effect yet that we expect to see going forward.

And then I don't know about you, I still detect a reluctance on enterprises' part for Chinese open weights. That is true. I would say that where we see there's no question that there is a big movement afoot to decreasing the costs. I think the first step is what can we do with proprietary smaller models that give us the expected response, the expected performance. but I also think that a number of them are starting to experiment with open weights including Chinese open weights which they want though to have more on-premise as opposed to having data moving out. So item three that I like, it seems like the burn, cash burn,

is not as worrisome as I would have thought, right? So they have raised $141.1 billion, which is insane. But they still hold $131.7 billion in cash and short-term investments. Yeah, I think that they have done a very good job in cash management and pushing some of their expenses towards warrants and debt, which I think is an interesting approach with interest rates rising and if they continue to rise, that may come back to bite them, but they do have enough cash. But that's where the performance of the company will play a big role going forward. So the next item I filed under things I like, but it's a bit of a mixed bag. So it seems like the adoption is now kind of broadening, right? So no single customer accounted for more than 10% of the revenue.

The top five customers' share fell from 36% to 21%. And then the customers that spend at least $100K grew from $950,000 to close to $12,000. But the thing that is a bit weird here is that 48% of the revenue still flows through the cloud hyperscalers. I guess it's not weird. That makes sense. Who also happens to compete with them? Exactly. I think that's both like and dislike, in a sense. There's a significant risk there. Look, the customer numbers and counts impressed me. I have to say that I was not expecting this type of broadening. What I'm watching for is I think what we just spoke about is whether these customers will continue spending the same amount,

number one, or whether the use of smaller models, the use of open-wage models will decrease that. Number two is whether they're owed by Anthropic, that consulting operation that they've set up, will enable them to continue penetrating to lower strata of the market. So, again, these are things both to like and not to like. And as you may already mention in your piece, your Meta and Microsoft, that were big users, are starting to pull back now on the use of Cloud. So that could also be a concern going forward But again, the numbers they posted in terms of customer counts, extremely impressive Yes, and then, let's see So what's next on my list is that the revenue per employee also has grown quite well, right?

So over the last 12 months, that went from 1.4 million per employee to 6.6 million, which, as you just mentioned, I guess the more forward deployed engineers they have, might that number slow down or even decrease, right? Actually, what I thought this number was impressive on, first of all, I think it sent a clear signal to more conventional enterprises of what an AI-first company can achieve. So that's a big point there. The second one, I think, is sent a signal to the software companies, including SaaS companies, which definitely do not have – I mean, they have better economics per employee than traditional enterprises. But that kind of number that Anthropic has posted is just the next level up.

So that is going to call into question what companies like ServiceNow and Salesforce and Workday and the like can do. And obviously, if you go further down to more traditional software companies, SAP, that will have an impact on. They'll have to rethink in terms of how they improve their numbers because now there is a bar to cross. And so before I go to the things I don't like Are there things you like that are not on my list? No, actually I think that it was I think to me It's interesting how the software development Has pulled the business along It tells you where we can, again, what we can do with AI and the right AI.

We'll see how it translates to other tasks. But I, again, so far so good. I mean, everything has been extremely impressive. Go ahead. So I guess there's one other thing on my list that I like, which is a bit of a mixed bag. So I'm just going to mention it here before we take it down later in my list of negatives. But basically, I guess that, you know, for a long time, my impression is that a lot of the revenue comes from API calls, right, so on Amazon. But I guess a lot of the revenue increasingly is going to cloud code and cloud code. Cloud. So at least there's a sign that maybe there's some notion of a product coming alive somehow,

which gives them maybe an opportunity to go beyond Claude Code. Maybe there will be Claude Legal, Claude Finance. I think they're trying for that. That would be the – it's a bit of a kind of a nascent signal that – I don't want to oversell it. Look, there's no question that the product market fit to software has been unbelievable, and it opened the eyes to many of their competitors and the whole world in general. whether they will be able to, and this is a statement not only for Anthropic, but we have OpenAI coming right behind them on the IPO scene, whether they will be able to create a product market fit for other areas. We mentioned legal, it could be finance, it could be financial operations, in other words. That, you know, insurance processing, that remains to be seen.

And it's also something that you mentioned in your piece, which I really like, which was in software, success makes you want to do more with it, right? You want to write more software. That does not translate to every other process. And so even if they find product market fit in another area, it doesn't imply that we will see the same kind of curves that we have seen with software. The token burn. Hey, before I go to my list of negatives, we briefly mentioned the revenue per employee is growing, and then you mentioned SaaS. What happened to the SaaS-pocalypse? Actually you know based on the quarterly results that companies are starting to announce for third quarter we starting to I won call it sasphocalypse 2 but there certainly some worries

So if you look at ServiceNow, if you look at Salesforce, and we'll see how by the end of the reporting season where we come out. But I think some of the worries have come back, maybe not to the extent that we were thinking about or that the community was thinking about early on, but certainly has not gone away, especially for the thing that we just mentioned here, that there might be other domains where you can find good product market fit, even if you may not find the acceleration that you found with software development. But is it just my pet peeve evangelist that the media kind of went nuts on the susspocalypse as a headline? I think they did, and we're always trying to find blemishes and trying to knock things down. But look, there's no question that many companies are starting to reexamine what percent of the packaged application they're actually using

and whether they can replicate, now that they have gotten the bug that they can develop software very quickly and very painlessly, if I can call that, whether they may be able to implement certain of these features on their own without having to pay these huge licenses. And that puts pressure on the SaaS application providers. By the way, speaking of this topic, I can't name people, but I talked to a very high person in one of these publicly traded SaaS, and the comment was, you know, you have all these people on LinkedIn saying, we're getting off your software because now I've discovered I can do this and that. And then when you look up whether the person is still using the software, yes, in fact, they're using it quite heavily. And look, the fact is, and I say this a lot to our portfolio companies, these applications, whether it is on-premise like SaaS and the like, whether it is SaaS applications like Workday, like ServiceNow, like Salesforce, they hold a lot of data now.

And so even if you don't use the business logic, if we can call it part of the application, they're not going to give you that data that they have now in their warehouses for free. And we see Salesforce already starting to make moves in that direction and charging for access to the data even if you're not using their application. So that's why I'm saying that I don't think we're going to have a saspocalypse in the way that it was envisioned, but we're definitely seeing certain implications from the success of the AI tools. All right. So things I don't like. So the bare case, right? So first of all, the commitments for compute, right? So $800 billion over the next 10 years. At least they don't use the word trillion.

Because one of my pet peeves in this era, Evangelos, is people throw off the word trillion right and left. As if a trillion is such a trivial thing, right? Yeah. Well, I mean, we're starting to use in the U.S. debt. So why not? Yeah, look, I agree with you, and I think what you need to consider here, what our listeners need to consider is the fact that on the one hand we have these commitments, on the other hand we don't know what is deliverable from the data center creator. So there's a lot of fudging, there's a lot of gray area there, which – One caveat, based on my reading, is that most of this can't be cancelled. I think probably something like 60-65% is non-cancellable, but there is a piece of it that is cancelled.

What's 65% of 800 billion? Yeah, okay. I mean, it's still a big number. I'm not trying to dismiss it. I'm just saying that I agree with you, but if you want to be very precise, there is a little bit of silver lining, yes. All right, so the next thing I don't like, I guess, is the training treadmill, right? So basically, I think it's still expensive to train these models, and you are under the gun to keep releasing them. Because imagine you're on Tropic or OpenAI and say, you know what? We have enough models. We're going to take the next year off or the next six months off and just help our customers use our existing models. I don't think that's going to be good for their brand or marketing, right? So, in other words, they're on this treadmill where they have to incur all of these expensive costs around training, right?

Yes. Yes, and I said... By the way, before you continue there, but if you talk to anyone in enterprise, they'll tell you, quite frankly, if they don't release another model for the next 6 to 12 months, we have more than enough to do. Exactly. I think that what you're seeing is that the enterprise is not changing, is not switching models as rapidly. there is interest as we talked before about decreasing the cost and I think these companies have been doing a good job in decreasing the cost per token but I don't think that we will see, especially as we move from experimentation to deployment we will not be able to see this type of model switching that even has been assumed because there is going to be updates obviously Every application does it, but it's not going to be at the pace that it is anticipated.

So I think that will slow down. And the question then becomes, what's the mode for these companies? Right, right. So, again, the third item I don't like is that prices are heading down, and you can see this through all sorts of third-party data, right? So the Chinese labs are producing tokens at an industrial scale, releasing their models as open weights. And I think in the next 6 to 12 months, we'll have respectable Western open weights models, right? So NVIDIA is starting to buy NeoLabs to beef up Mematron. Reflection has an open weights model. thinking machines will probably continue to release open waste model. And then I know some initiatives that are going to go do kind of a full open source model initiative, right?

So I think the pressure from some notion of open is real. And people always say, well, the open weights are just distilling the proprietary models, right? But then as long as the proprietary models keep improving, the open weights models are going to improve. So the analogy that I've come to like is the water skiing, right? A water skier is being pulled by a speedboat, so the water skier can go as fast as a speedboat or maybe even faster, but can never get ahead of the speedboat. Yeah, that's right. Right? That's right. So, you know, prices are heading down. I think that's going to continue, right? Yeah. And how does that – am I right to consider that a negative?

Yeah, I think it's kind of what I was saying also before We will have to see how their revenue growth Starting with Anthropic But also going to OpenAI Which will follow quickly Behind them on the IPO market I think we'll have to see whether that revenue growth Continues to be as impressive as it's been Up through the first half of 26 So another negative is coding is disproportionately carrying the business, right? So some estimates place it at 70% to 80%. And you're a former VC. Isn't it an adage in the VC world that your first customers are usually not representative of the broader market, right? And in this case, as you point out, right, so coding is expansive, right? So people start coding, they write code more, they use more tokens, but a lot of knowledge work is compressive.

So I want to summarize it all. I want to read a bunch of papers, but only give me like bullet points, right? So, look, I think that, as I said before, identifying coding as a use case was a brilliant, I don't know if it was an accident or if it was deliberate, but it was a brilliant product market fit, and that has allowed not only Anthropic but also other companies to be successful. identifying additional such use cases will be very important for the future otherwise I think we will see some very unexpected outcomes so on the coding front I guess because as you said they identify the market now it's more competitive right so everyone is going to coding

I mean, OpenAI is a lot of revenue in coding as well, right? So I happen to use open code with open routers so I can switch models. But the general impression I get is that most people settle on cloud code and maybe, you know, they don't really move off of it quite as easily as I would think. But that's just anecdotal data on my part, yeah. Actually, I would say I would put it both. During experimentation, I mean, if you look at enterprises that are experimenting with AI, and most of them do, including many of these 12,000 that they list as customers, and during that phase there is a lot of switching between models, between tools, but once you start moving forward and you start going to deployment, You're making some decisions. Then the switching, whether it is at the model level or the application level, goes away.

Because the enterprise cannot afford it, right? So this is something that I think this old bioanthropic and this consulting partnerships that they have could be extremely beneficial for them. because if an organization adopts for real their tools, it will be very difficult then, and we seen it over the years in information technology it will be very difficult to diswatch them And that then becomes their mode There is one upcoming trend which will happen in the next few months early next year probably is that a lot of people are working on shared memory, shared context, right? One of the things is that when you use a coding tool and then you try to switch to another, then you lose a lot of the context. A lot of context. A lot of context and memory.

But a lot of people have noticed this and are upset about it. So I know of at least one open source project that I actually have been quite impressed on who are going to release something early next year. and this will solve your switching costs, I think. Cost, yeah. So, again, I think we need to keep monitoring what's happening in the broader community as opposed to just, you know, in one of the new labs. Go ahead. All right. So one thing that seems to be a trend whenever these tech companies go public, the co-founders carry control through a class of shares. So in this case, the seven co-founders control 50.1% of the vote, which I thought their brand was – I guess their brand is, you know, we're going to shepherd AI to safety, so we're the right seven people to do that, so we should have control, right? I agree with your concern.

I mean, the way I put it is that the public shareholders buy economics, whereas they do not buy control. and that is very different from what we've seen in the past. In the past. Yeah, yeah, yeah. What about, so I brought this up earlier, but partners who are also rivals. Exactly, I agree. I agree. Google, Amazon, Microsoft. Yeah, exactly. Right, so I agree. That's a negative. All right, so do you have any negatives? I would say the dilution component to me is important because they have certain milestones that if they're rich, they are able to issue more stock. And also they are assigning stock to philanthropy. And that can lead – I mean, again, I'm not against philanthropy.

I'm just saying that from a shareholder perspective, that can lead to significant dilution. And that will, I mean, again, you can put it as negative because we always think of dilution as a negative thing. At the very least, it's very different from what we've seen in past IPOs. All right. So second topic is a quick one. in my last newsletter I posted a cheeky graphic where I said SpaceX is becoming a Neon cloud one more thing I'm sorry I apologize for breaking the rhythm sales and marketing rose by 26 times revenue rose by 14x to me that was also a negative just let's move on All right. So topic number two is SpaceX becoming a neocloud. Well, according to their earnings, that seems to be the case.

Right. So the I, I contributed four hundred thirty one in operating profit, but they spent sixteen point four billion. And so what does this mean in terms of things moving forward? So I identified three things. So let's see if you agree with any of these evangelists or probably you'll agree with all of them. Right. So one is for for this to work out for SpaceX, one, they need to have continued access to cheap capital. Yes. And in fact, I don't know if you noticed, but Elon tweeted something where there will be a bifurcation in neoclouds. neoclouds, those who can access cheap capital and those who can't. And those who can access cheap capital usually have another business, meaning, in his case, Starlink and all of these. And then the pure neoclouds are screwed, basically, is what he said.

Yeah, I agree with that observation, but I will also say the following. SpaceX is getting into too many businesses. I mean, you saw that they bought Spectrum. now they want to be a telco and they may become a car company exactly so to a certain extent they are I think they are spreading the risk compared to say an anthropic that they have one business or I guess sticking with the neoclid compared to Corweave or Landa or any of those guys But on the other hand, there is also each of these businesses that they require significant capital. I mean, TerraFarm needs $100 billion, okay? To set up a proper telco, you need, I don't know, $30 to $50 billion. I mean, these are billion, right?

So, again, I don't envy him for having to make certain decisions. Yes, it spreads the risk, but also he's getting into businesses that are very capital intensive. And by the way, I think in his S1, he identified the addressable market as $40 trillion. Which, by the way, I just heard an interesting stat. From 1990 to 2025, the two largest software companies in history, Microsoft and Apple, combined, their total revenue was not even $10 trillion. Right? But Elon somehow is going to get to $40 trillion. Whatever works. All right. Number two that needs to go right. You need to keep growing with solvent customers, right? But right now, their neocloud customers are anthropic, not profitable.

Reflection AI, not profitable. So you need to get to enterprise customers who are willing to pay, lock them in for long-term contracts. But it's a commodity business. It's not like you have a charge premium pricing, right? Look, I believe that SpaceX got into the NeoCloud business not by design but by accident. They had the capacity, good for them, that they managed to have the capacity to create that architecture around Colossus. And all of a sudden they came to the market and they said, we have that capability. And by the way, now they have – And the reason they have capacity is he wanted to build models, right? Exactly, exactly. And now there are a lot more, by the way, a lot of companies. I mean, even Meta now is talking about, hey, we have capacity too because we have access to this data center.

So I'm very concerned about where that is going. I mean, on the one hand, I'm concerned about whether we will be able to fulfill all of the promises that data center developers have made and bring up the capacity. But on the other hand, there is quite a bit of capacity that is becoming available and whether it will continue to be filled profitably, right? That remains a question. Here's another source of capacity that I'm imagining and tell me if I'm crazy. Right. So look for look, look to next year. You have all these neo labs who raise so much money, who bought compute in advance. Right. To lock it in. Now they have to show growth to raise their next round. Well, I can show growth. I can sell my capacity. Yeah, exactly.

And that's exactly what SpaceX has done, in my opinion, with their data center. Okay. So do you think this will happen? Does Neo Labs may share capacity next year? I actually think, I mean, to me, what is more important, I'll say this as an investor, is exactly what you said before, I mean, just before that, which was now these companies will come to their next round of funding, and they will need to show that they have been meeting the claims that they were making before. And I think a lot of investors, both existing and prospective, are going to be disappointed at what will be posted. And I think we will have a crisis around that. Whether NVIDIA will be able to backstop everything, I do not know. But we will definitely have that. But right now, VCs only value growth.

So here's how I would play the game, right? So I'm a NeoLab. I have all this capacity. To show growth, I'll sell my capacity. But then I'll also release a small model to show, hey, look, we built a model. And this is just the first. We're going to build more. But in the meantime, look at our revenue. Yeah. Right? Yeah, I don't know. I mean, I guess I'm getting too carmageony for making optimistic statements. Which, by the way, leads me to the third thing that has to go right, which plays into everything we just have been talking about, which is the spot prices for compute capacity in these near cloud data centers has to continue to go up. It's going to go up, yeah. And what do you think? whether it will continue to go up I mean a lot of it depends right now it's going up

right now it's going up because there is more demand for this experimentation than there is supply I expect that next year towards the end if everything goes as planned we will start seeing much more equilibrium than we can today All right. Topic number three. Obviously, the elections in the U.S. are on Tuesday, and it's widely anticipated that the Democrats will gain control of at least one House of Congress, maybe both. and if that's the case then discussions around what to do about ai may grow more intense and even if they fall short and let's say they don't win any of the houses of congress the belief is that in the 2028 election cycle ai and how to regulate ai will be some sort of

litmus test in the democratic side of the nomination process for president. So with that said, I've started to gather some proposals that are up in the air. Let's put it that way, right? So from the democratic side of the House. So I'm going to run it by Evangelist just to get his quick reaction and then I'll react too, right? So the first set of proposals of center around self-improvement and the development of models, right? So proposal one maybe we should pause recursive self Yeah, pause recursive self-improvement. I think that I agree with that. I think we need, and maybe not at the very least, study it a lot more

and try to understand it a lot more than we do today. Wow. I am shocked. Because my reaction to that proposal is this seems a bit crazy in the following sense, right? So first of all, you have to define for me precisely what recursive self-improvement is. That's what I said. Because if I'm using AI to code, is that recursive self-improvement? So someone has to define. It depends what you're coding, though, right? Yeah, yeah. I'm coding the model, right? Or is it only if I let the AI run wild and create itself, right? Yeah. You know what I mean? But if you're restricting me from using AI to even work on the next generation model, that's impossible. How is that even enforceable, right? Well, actually, it comes down to what I said and what maybe you better elaborate,

which is we need to define what we mean by recursive self-improvement because today we're starting to use that term, but there are many interpretations of the term. And the more we understand it, I think we need to define those interpretations. So that's why I made a statement that I did. Yeah, I'm a little wary of that. The second thing around self-improvement is this, which is use only vetted models to develop successors. That makes sense. Yeah, yeah, yeah, yeah. So here's my pushback to that, right? So who's going to certify the models to vet them? Right? So, first of all, it seems like that seems to be a foundational step.

Right? So, and is it, you know, I may be using an internal model that hasn't been released to the public yet to do self-improvement. Is that allowed? Or am I only supposed to use public models that have so-called been vetted? And by the way, we don't have any vetting process right now anyway. Yes. So I think as we find, Ben, I think as we find with every new technology and government involvement, is that the people in the government who want to be involved are behind where the technology is. I mean, that's an adage that everybody accepts. So how are you going to do this type of definitional work? We talked about recursive self-improvement, how you're going to do vetting work. It will really depend on who is doing it and how they got there to be doing it.

And I think that if we assume that some bureaucrat who is in some government organization organization is anointed to do this vetting and then receives all sorts of side input from people who call them lobbyists, call it hangers-on, whatever, I think it's going to be a complete failure. So I think what we are seeing here, and based on what you just said up to this point, is that there is a – conceptually, these ideas make sense, but who is going to be doing it and what kind of detail is going to be provided around these proposals is probably more important than the proposal itself. All right. Next one is wait before using new models for internal R&D. So wait could be anywhere from three to six, nine months, right?

So it would have to be models that have been deployed three, six, nine months out to the world. And then you can use it for your self-improvement. I think that's crazy. I think that's crazy. Yeah. Yeah. And also, even let's say for the sake of argument, you do that. just because you deployed it to the public and nothing happened, that doesn't mean that when you use it for internal research. Exactly. Right? So it's safe because it's a different use case. Yeah. I guess this one has already been proposed, which is coordinate slowdowns with competitors, but that's not really, I don't think, that's a prisoner's dilemma scenario. It is, and I think that I've seen this, or I've listened to several debates along these lines,

and nobody believes that it can lead to anything successful. All right. So then the next set of proposals is around testing, right? So I guess give independent evaluators employee-level access. I think that's already happening. I think Antropic already is doing this for certain agencies, right? But again, it also depends, as I said before, who is doing it and how they got there to be anointed in that position. So it's not only the permission aspect, but it's also the characteristics of that person or persons who are doing the evaluation. yeah I think Antropic is working with a non-profit evaluator but I think as you pointed out so the government agencies will struggle to hire people

because basically the compensation is bad and it's more bureaucratic the tools you have to use inside the government are not as good not many people will want to work in the government So one idea that I've heard, I've been in a meeting where people are proposing, what we need to do is encourage the emergence of private testers equivalent to the credit rating agencies. But as you know, right, so they can be captured as well, right? In the global financial crisis, the rating agencies were paid by the banks who secured it as they raised it, right? so there's another model and I'm not sure if you're familiar there's an outfit in Chicago called underwriter laboratories yes I do I think if you buy like a fridge or something there might be like a seal that's right I don't know much about them

but they have a good reputation I don't know what their relationship is with the companies that they're raiding no actually in Europe you have corresponding organizations. Actually, they're better organized in that front than we are. These organizations try to make a leap, in a sense, a jump from what they have been traditionally testing, which is devices that have a consumer exposure to testing other technologies. It's not been as successful, but I think you're on the right track there. But the point is that they're part of this long tradition around test engineering. Yes. that the AI community may be worth listening to them.

So another proposal is share models with international safety institutes in other countries. That's not going to happen under the current administration. I don't think it's going to happen in general. I mean, it may happen partially, but then that will make it die quickly. I think that's really hard to implement. So in the financial services, they have this notion of stress testing. Yeah. So that's another proposal is come up with an analog for the AI models, which is there's some routine regular stress testing happening. I think that makes sense. All right. So then very quickly, a bucket around your training security. One is lock down your training environments. I guess this is because of the hugging face incident.

I don't know exactly what the specific proposal there. I know that OpenAI just announced that they changed the way they train models, which is they make a copy of the entire Internet, and then the model training is only happening in this kind of sandbox copy of the Internet. It's a larger sandbox. Yeah, yeah. So the agent is free to do all sorts of Internet stuff, but in this copy of the Internet. Yeah. Look, I will also say that it would be a good first step if the government requires these companies to fully disclose all of these incidents. Because I'm very concerned at the fact that we are being drip-fed more breakouts of these agents. And I think that there are a lot more, I'm not trying to start a conspiracy rumor,

but I think there are a lot more problems than we know about. and it would be even more unfortunate if there are problems that the AI providers do not know about. But I think the neo labs know a lot more than they're letting out and there are reasons for that as well. But I think if there is somebody that could require them to be full disclosure, I think that would be a particularly good first step. Yeah, in fact, disclosure is one of the proposals. and then I'll rattle off quickly because I know you have to go. Protect whistleblowers is another one. Reward internal escalation, right? So if someone internally in the lab sees something, they should not be punished. In fact, they should be even rewarded for raising alarms and so on and so forth. So provided that is real, right?

I mean, I think that the gentleman from Anthropical Design created a lot of hubbub, but there wasn't a lot of meat behind what he said, right? I mean, so to me, in this time when we have everybody on the edge, both employees because their jobs may be affected, consumers who hear rumors about humanity's demise, I think it would be important, particularly for the labs the labs and for the people who take these steps, whether they're whistleblowers or whatever, to be very serious about it and very cautious about what they're causing. And with that, thank you, Evangelos. Again, episode notes on the dataexchange.media.